{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业\n",
    "### 分析指标一：破净股比例\n",
    "#### 破净股：股票的每股市场价格低于它每股净资产价格\n",
    "#### 比值 = 破净股上市公司数量/上市公司总数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_stock = list(get_all_securities().index)  # 聚宽接口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.XSHG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-07-15</th>\n",
       "      <td>2779.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-16</th>\n",
       "      <td>2705.8700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-17</th>\n",
       "      <td>2684.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-18</th>\n",
       "      <td>2778.3700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-21</th>\n",
       "      <td>2861.4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-22</th>\n",
       "      <td>2846.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-23</th>\n",
       "      <td>2837.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-24</th>\n",
       "      <td>2910.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-25</th>\n",
       "      <td>2865.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-28</th>\n",
       "      <td>2903.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-29</th>\n",
       "      <td>2850.3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-30</th>\n",
       "      <td>2836.6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-31</th>\n",
       "      <td>2775.7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>2801.8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-04</th>\n",
       "      <td>2741.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-05</th>\n",
       "      <td>2690.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-06</th>\n",
       "      <td>2719.3700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-07</th>\n",
       "      <td>2727.5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-08</th>\n",
       "      <td>2605.7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-11</th>\n",
       "      <td>2470.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-12</th>\n",
       "      <td>2457.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-13</th>\n",
       "      <td>2446.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-14</th>\n",
       "      <td>2437.0800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-15</th>\n",
       "      <td>2450.6100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-18</th>\n",
       "      <td>2319.8700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-19</th>\n",
       "      <td>2344.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-20</th>\n",
       "      <td>2523.2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-21</th>\n",
       "      <td>2431.7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-22</th>\n",
       "      <td>2405.2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-25</th>\n",
       "      <td>2413.3700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-25</th>\n",
       "      <td>3219.4179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-28</th>\n",
       "      <td>3217.5346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-29</th>\n",
       "      <td>3224.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>3218.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>3272.0800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-12</th>\n",
       "      <td>3358.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>3359.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-14</th>\n",
       "      <td>3340.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>3332.1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-16</th>\n",
       "      <td>3336.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-19</th>\n",
       "      <td>3312.6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-20</th>\n",
       "      <td>3328.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-21</th>\n",
       "      <td>3325.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22</th>\n",
       "      <td>3312.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-23</th>\n",
       "      <td>3278.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>3251.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27</th>\n",
       "      <td>3254.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-28</th>\n",
       "      <td>3269.2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29</th>\n",
       "      <td>3272.7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30</th>\n",
       "      <td>3224.5300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>3225.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>3271.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>3277.4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>3320.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>3312.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>3373.7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-10</th>\n",
       "      <td>3360.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-11</th>\n",
       "      <td>3342.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-12</th>\n",
       "      <td>3338.6800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-13</th>\n",
       "      <td>3310.1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.XSHG\n",
       "2008-07-15    2779.4500\n",
       "2008-07-16    2705.8700\n",
       "2008-07-17    2684.7800\n",
       "2008-07-18    2778.3700\n",
       "2008-07-21    2861.4200\n",
       "2008-07-22    2846.1200\n",
       "2008-07-23    2837.8500\n",
       "2008-07-24    2910.2900\n",
       "2008-07-25    2865.1000\n",
       "2008-07-28    2903.0100\n",
       "2008-07-29    2850.3100\n",
       "2008-07-30    2836.6700\n",
       "2008-07-31    2775.7200\n",
       "2008-08-01    2801.8200\n",
       "2008-08-04    2741.7400\n",
       "2008-08-05    2690.7500\n",
       "2008-08-06    2719.3700\n",
       "2008-08-07    2727.5800\n",
       "2008-08-08    2605.7200\n",
       "2008-08-11    2470.0700\n",
       "2008-08-12    2457.2000\n",
       "2008-08-13    2446.3000\n",
       "2008-08-14    2437.0800\n",
       "2008-08-15    2450.6100\n",
       "2008-08-18    2319.8700\n",
       "2008-08-19    2344.4700\n",
       "2008-08-20    2523.2800\n",
       "2008-08-21    2431.7200\n",
       "2008-08-22    2405.2300\n",
       "2008-08-25    2413.3700\n",
       "...                 ...\n",
       "2020-09-25    3219.4179\n",
       "2020-09-28    3217.5346\n",
       "2020-09-29    3224.3600\n",
       "2020-09-30    3218.0500\n",
       "2020-10-09    3272.0800\n",
       "2020-10-12    3358.4700\n",
       "2020-10-13    3359.7500\n",
       "2020-10-14    3340.7800\n",
       "2020-10-15    3332.1800\n",
       "2020-10-16    3336.3600\n",
       "2020-10-19    3312.6700\n",
       "2020-10-20    3328.1000\n",
       "2020-10-21    3325.0200\n",
       "2020-10-22    3312.5000\n",
       "2020-10-23    3278.0000\n",
       "2020-10-26    3251.1200\n",
       "2020-10-27    3254.3200\n",
       "2020-10-28    3269.2400\n",
       "2020-10-29    3272.7300\n",
       "2020-10-30    3224.5300\n",
       "2020-11-02    3225.1200\n",
       "2020-11-03    3271.0700\n",
       "2020-11-04    3277.4400\n",
       "2020-11-05    3320.1300\n",
       "2020-11-06    3312.1600\n",
       "2020-11-09    3373.7300\n",
       "2020-11-10    3360.1500\n",
       "2020-11-11    3342.2000\n",
       "2020-11-12    3338.6800\n",
       "2020-11-13    3310.1000\n",
       "\n",
       "[3000 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取上证指数 3000个交易日 的信息\n",
    "df = history(count = 3000, unit = '1d',security_list = '000001.XSHG', field = 'close',df = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-07-15</th>\n",
       "      <td>2779.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-16</th>\n",
       "      <td>2705.8700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-17</th>\n",
       "      <td>2684.7800</td>\n",
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       "    <tr>\n",
       "      <th>2008-07-18</th>\n",
       "      <td>2778.3700</td>\n",
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       "    <tr>\n",
       "      <th>2008-07-21</th>\n",
       "      <td>2861.4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-22</th>\n",
       "      <td>2846.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-23</th>\n",
       "      <td>2837.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-24</th>\n",
       "      <td>2910.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-25</th>\n",
       "      <td>2865.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-28</th>\n",
       "      <td>2903.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-29</th>\n",
       "      <td>2850.3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-30</th>\n",
       "      <td>2836.6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-31</th>\n",
       "      <td>2775.7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>2801.8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-04</th>\n",
       "      <td>2741.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-05</th>\n",
       "      <td>2690.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-06</th>\n",
       "      <td>2719.3700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-07</th>\n",
       "      <td>2727.5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-08</th>\n",
       "      <td>2605.7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-11</th>\n",
       "      <td>2470.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-12</th>\n",
       "      <td>2457.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-13</th>\n",
       "      <td>2446.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-14</th>\n",
       "      <td>2437.0800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-15</th>\n",
       "      <td>2450.6100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-18</th>\n",
       "      <td>2319.8700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-19</th>\n",
       "      <td>2344.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-20</th>\n",
       "      <td>2523.2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-21</th>\n",
       "      <td>2431.7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-22</th>\n",
       "      <td>2405.2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-25</th>\n",
       "      <td>2413.3700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-25</th>\n",
       "      <td>3219.4179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-28</th>\n",
       "      <td>3217.5346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-29</th>\n",
       "      <td>3224.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>3218.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>3272.0800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-12</th>\n",
       "      <td>3358.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>3359.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-14</th>\n",
       "      <td>3340.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>3332.1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-16</th>\n",
       "      <td>3336.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-19</th>\n",
       "      <td>3312.6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-20</th>\n",
       "      <td>3328.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-21</th>\n",
       "      <td>3325.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22</th>\n",
       "      <td>3312.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-23</th>\n",
       "      <td>3278.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>3251.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27</th>\n",
       "      <td>3254.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-28</th>\n",
       "      <td>3269.2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29</th>\n",
       "      <td>3272.7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30</th>\n",
       "      <td>3224.5300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>3225.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>3271.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>3277.4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>3320.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>3312.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>3373.7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-10</th>\n",
       "      <td>3360.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-11</th>\n",
       "      <td>3342.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-12</th>\n",
       "      <td>3338.6800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-13</th>\n",
       "      <td>3310.1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.XSHG\n",
       "2008-07-15    2779.4500\n",
       "2008-07-16    2705.8700\n",
       "2008-07-17    2684.7800\n",
       "2008-07-18    2778.3700\n",
       "2008-07-21    2861.4200\n",
       "2008-07-22    2846.1200\n",
       "2008-07-23    2837.8500\n",
       "2008-07-24    2910.2900\n",
       "2008-07-25    2865.1000\n",
       "2008-07-28    2903.0100\n",
       "2008-07-29    2850.3100\n",
       "2008-07-30    2836.6700\n",
       "2008-07-31    2775.7200\n",
       "2008-08-01    2801.8200\n",
       "2008-08-04    2741.7400\n",
       "2008-08-05    2690.7500\n",
       "2008-08-06    2719.3700\n",
       "2008-08-07    2727.5800\n",
       "2008-08-08    2605.7200\n",
       "2008-08-11    2470.0700\n",
       "2008-08-12    2457.2000\n",
       "2008-08-13    2446.3000\n",
       "2008-08-14    2437.0800\n",
       "2008-08-15    2450.6100\n",
       "2008-08-18    2319.8700\n",
       "2008-08-19    2344.4700\n",
       "2008-08-20    2523.2800\n",
       "2008-08-21    2431.7200\n",
       "2008-08-22    2405.2300\n",
       "2008-08-25    2413.3700\n",
       "...                 ...\n",
       "2020-09-25    3219.4179\n",
       "2020-09-28    3217.5346\n",
       "2020-09-29    3224.3600\n",
       "2020-09-30    3218.0500\n",
       "2020-10-09    3272.0800\n",
       "2020-10-12    3358.4700\n",
       "2020-10-13    3359.7500\n",
       "2020-10-14    3340.7800\n",
       "2020-10-15    3332.1800\n",
       "2020-10-16    3336.3600\n",
       "2020-10-19    3312.6700\n",
       "2020-10-20    3328.1000\n",
       "2020-10-21    3325.0200\n",
       "2020-10-22    3312.5000\n",
       "2020-10-23    3278.0000\n",
       "2020-10-26    3251.1200\n",
       "2020-10-27    3254.3200\n",
       "2020-10-28    3269.2400\n",
       "2020-10-29    3272.7300\n",
       "2020-10-30    3224.5300\n",
       "2020-11-02    3225.1200\n",
       "2020-11-03    3271.0700\n",
       "2020-11-04    3277.4400\n",
       "2020-11-05    3320.1300\n",
       "2020-11-06    3312.1600\n",
       "2020-11-09    3373.7300\n",
       "2020-11-10    3360.1500\n",
       "2020-11-11    3342.2000\n",
       "2020-11-12    3338.6800\n",
       "2020-11-13    3310.1000\n",
       "\n",
       "[3000 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除 空值\n",
    "df.dropna(inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.orm.query.Query at 0x7f4285c73c50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查询 破净的上市公司\n",
    "# query : 查询\n",
    "# filter : 筛选\n",
    "# valuation : 内置市值数据对象\n",
    "# code : 股票代码\n",
    "# pb_ratio : 股票的市净率\n",
    "# 将 pb_ratio < 1 的股票视为破净股\n",
    "q = query(valuation.code,valuation.pb_ratio).filter(valuation.pb_ratio < 1)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.XSHG</th>\n",
       "      <th>pbcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-07-15</th>\n",
       "      <td>2779.4500</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-16</th>\n",
       "      <td>2705.8700</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-17</th>\n",
       "      <td>2684.7800</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-18</th>\n",
       "      <td>2778.3700</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-21</th>\n",
       "      <td>2861.4200</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-22</th>\n",
       "      <td>2846.1200</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-23</th>\n",
       "      <td>2837.8500</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-24</th>\n",
       "      <td>2910.2900</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-25</th>\n",
       "      <td>2865.1000</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-28</th>\n",
       "      <td>2903.0100</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-29</th>\n",
       "      <td>2850.3100</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-30</th>\n",
       "      <td>2836.6700</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-31</th>\n",
       "      <td>2775.7200</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>2801.8200</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-04</th>\n",
       "      <td>2741.7400</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-05</th>\n",
       "      <td>2690.7500</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-06</th>\n",
       "      <td>2719.3700</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-07</th>\n",
       "      <td>2727.5800</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-08</th>\n",
       "      <td>2605.7200</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-11</th>\n",
       "      <td>2470.0700</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-12</th>\n",
       "      <td>2457.2000</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-13</th>\n",
       "      <td>2446.3000</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-14</th>\n",
       "      <td>2437.0800</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-15</th>\n",
       "      <td>2450.6100</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-18</th>\n",
       "      <td>2319.8700</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-19</th>\n",
       "      <td>2344.4700</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-20</th>\n",
       "      <td>2523.2800</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-21</th>\n",
       "      <td>2431.7200</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-22</th>\n",
       "      <td>2405.2300</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-25</th>\n",
       "      <td>2413.3700</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-25</th>\n",
       "      <td>3219.4179</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-28</th>\n",
       "      <td>3217.5346</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-29</th>\n",
       "      <td>3224.3600</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>3218.0500</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>3272.0800</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-12</th>\n",
       "      <td>3358.4700</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>3359.7500</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-14</th>\n",
       "      <td>3340.7800</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>3332.1800</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-16</th>\n",
       "      <td>3336.3600</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-19</th>\n",
       "      <td>3312.6700</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-20</th>\n",
       "      <td>3328.1000</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-21</th>\n",
       "      <td>3325.0200</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22</th>\n",
       "      <td>3312.5000</td>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-23</th>\n",
       "      <td>3278.0000</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>3251.1200</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27</th>\n",
       "      <td>3254.3200</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-28</th>\n",
       "      <td>3269.2400</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29</th>\n",
       "      <td>3272.7300</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30</th>\n",
       "      <td>3224.5300</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>3225.1200</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>3271.0700</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>3277.4400</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>3320.1300</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>3312.1600</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>3373.7300</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-10</th>\n",
       "      <td>3360.1500</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-11</th>\n",
       "      <td>3342.2000</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-12</th>\n",
       "      <td>3338.6800</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-13</th>\n",
       "      <td>3310.1000</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.XSHG  pbcount\n",
       "2008-07-15    2779.4500       63\n",
       "2008-07-16    2705.8700       65\n",
       "2008-07-17    2684.7800       65\n",
       "2008-07-18    2778.3700       63\n",
       "2008-07-21    2861.4200       62\n",
       "2008-07-22    2846.1200       62\n",
       "2008-07-23    2837.8500       62\n",
       "2008-07-24    2910.2900       61\n",
       "2008-07-25    2865.1000       62\n",
       "2008-07-28    2903.0100       61\n",
       "2008-07-29    2850.3100       62\n",
       "2008-07-30    2836.6700       62\n",
       "2008-07-31    2775.7200       62\n",
       "2008-08-01    2801.8200       62\n",
       "2008-08-04    2741.7400       62\n",
       "2008-08-05    2690.7500       65\n",
       "2008-08-06    2719.3700       62\n",
       "2008-08-07    2727.5800       63\n",
       "2008-08-08    2605.7200       72\n",
       "2008-08-11    2470.0700       80\n",
       "2008-08-12    2457.2000       82\n",
       "2008-08-13    2446.3000       83\n",
       "2008-08-14    2437.0800       81\n",
       "2008-08-15    2450.6100       81\n",
       "2008-08-18    2319.8700       97\n",
       "2008-08-19    2344.4700       92\n",
       "2008-08-20    2523.2800       77\n",
       "2008-08-21    2431.7200       83\n",
       "2008-08-22    2405.2300       87\n",
       "2008-08-25    2413.3700       86\n",
       "...                 ...      ...\n",
       "2020-09-25    3219.4179      347\n",
       "2020-09-28    3217.5346      353\n",
       "2020-09-29    3224.3600      355\n",
       "2020-09-30    3218.0500      360\n",
       "2020-10-09    3272.0800      339\n",
       "2020-10-12    3358.4700      319\n",
       "2020-10-13    3359.7500      320\n",
       "2020-10-14    3340.7800      332\n",
       "2020-10-15    3332.1800      333\n",
       "2020-10-16    3336.3600      327\n",
       "2020-10-19    3312.6700      333\n",
       "2020-10-20    3328.1000      328\n",
       "2020-10-21    3325.0200      332\n",
       "2020-10-22    3312.5000      336\n",
       "2020-10-23    3278.0000      344\n",
       "2020-10-26    3251.1200      345\n",
       "2020-10-27    3254.3200      354\n",
       "2020-10-28    3269.2400      352\n",
       "2020-10-29    3272.7300      361\n",
       "2020-10-30    3224.5300      389\n",
       "2020-11-02    3225.1200      391\n",
       "2020-11-03    3271.0700      372\n",
       "2020-11-04    3277.4400      374\n",
       "2020-11-05    3320.1300      362\n",
       "2020-11-06    3312.1600      364\n",
       "2020-11-09    3373.7300      355\n",
       "2020-11-10    3360.1500      355\n",
       "2020-11-11    3342.2000      356\n",
       "2020-11-12    3338.6800      353\n",
       "2020-11-13    3310.1000      357\n",
       "\n",
       "[3000 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取（上证指数交易日）每一个交易日 破净的上市公司数量\n",
    "# get_fundamentals（） 查询某一交易日的股票财务数信息\n",
    "# get_fundamentls_continuously(） 查询多个交易日的股票财务信息\n",
    "# get_fundamentls_continuously(query_object, end_date=None, count=None) \n",
    "\n",
    "df['pbcount'] = [len(get_fundamentals(q,date=i)) for i in df.index]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.XSHG</th>\n",
       "      <th>pbcount</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-07-15</th>\n",
       "      <td>2779.4500</td>\n",
       "      <td>63</td>\n",
       "      <td>1587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-16</th>\n",
       "      <td>2705.8700</td>\n",
       "      <td>65</td>\n",
       "      <td>1589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-17</th>\n",
       "      <td>2684.7800</td>\n",
       "      <td>65</td>\n",
       "      <td>1589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-18</th>\n",
       "      <td>2778.3700</td>\n",
       "      <td>63</td>\n",
       "      <td>1589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-21</th>\n",
       "      <td>2861.4200</td>\n",
       "      <td>62</td>\n",
       "      <td>1589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-22</th>\n",
       "      <td>2846.1200</td>\n",
       "      <td>62</td>\n",
       "      <td>1589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-23</th>\n",
       "      <td>2837.8500</td>\n",
       "      <td>62</td>\n",
       "      <td>1591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-24</th>\n",
       "      <td>2910.2900</td>\n",
       "      <td>61</td>\n",
       "      <td>1591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-25</th>\n",
       "      <td>2865.1000</td>\n",
       "      <td>62</td>\n",
       "      <td>1591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-28</th>\n",
       "      <td>2903.0100</td>\n",
       "      <td>61</td>\n",
       "      <td>1592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-29</th>\n",
       "      <td>2850.3100</td>\n",
       "      <td>62</td>\n",
       "      <td>1592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-30</th>\n",
       "      <td>2836.6700</td>\n",
       "      <td>62</td>\n",
       "      <td>1592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-31</th>\n",
       "      <td>2775.7200</td>\n",
       "      <td>62</td>\n",
       "      <td>1593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>2801.8200</td>\n",
       "      <td>62</td>\n",
       "      <td>1593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-04</th>\n",
       "      <td>2741.7400</td>\n",
       "      <td>62</td>\n",
       "      <td>1593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-05</th>\n",
       "      <td>2690.7500</td>\n",
       "      <td>65</td>\n",
       "      <td>1593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-06</th>\n",
       "      <td>2719.3700</td>\n",
       "      <td>62</td>\n",
       "      <td>1595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-07</th>\n",
       "      <td>2727.5800</td>\n",
       "      <td>63</td>\n",
       "      <td>1595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-08</th>\n",
       "      <td>2605.7200</td>\n",
       "      <td>72</td>\n",
       "      <td>1595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-11</th>\n",
       "      <td>2470.0700</td>\n",
       "      <td>80</td>\n",
       "      <td>1596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-12</th>\n",
       "      <td>2457.2000</td>\n",
       "      <td>82</td>\n",
       "      <td>1596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-13</th>\n",
       "      <td>2446.3000</td>\n",
       "      <td>83</td>\n",
       "      <td>1597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-14</th>\n",
       "      <td>2437.0800</td>\n",
       "      <td>81</td>\n",
       "      <td>1597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-15</th>\n",
       "      <td>2450.6100</td>\n",
       "      <td>81</td>\n",
       "      <td>1597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-18</th>\n",
       "      <td>2319.8700</td>\n",
       "      <td>97</td>\n",
       "      <td>1598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-19</th>\n",
       "      <td>2344.4700</td>\n",
       "      <td>92</td>\n",
       "      <td>1598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-20</th>\n",
       "      <td>2523.2800</td>\n",
       "      <td>77</td>\n",
       "      <td>1598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-21</th>\n",
       "      <td>2431.7200</td>\n",
       "      <td>83</td>\n",
       "      <td>1598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-22</th>\n",
       "      <td>2405.2300</td>\n",
       "      <td>87</td>\n",
       "      <td>1598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-25</th>\n",
       "      <td>2413.3700</td>\n",
       "      <td>86</td>\n",
       "      <td>1598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-25</th>\n",
       "      <td>3219.4179</td>\n",
       "      <td>347</td>\n",
       "      <td>4029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-28</th>\n",
       "      <td>3217.5346</td>\n",
       "      <td>353</td>\n",
       "      <td>4036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-29</th>\n",
       "      <td>3224.3600</td>\n",
       "      <td>355</td>\n",
       "      <td>4038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>3218.0500</td>\n",
       "      <td>360</td>\n",
       "      <td>4041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>3272.0800</td>\n",
       "      <td>339</td>\n",
       "      <td>4041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-12</th>\n",
       "      <td>3358.4700</td>\n",
       "      <td>319</td>\n",
       "      <td>4042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>3359.7500</td>\n",
       "      <td>320</td>\n",
       "      <td>4043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-14</th>\n",
       "      <td>3340.7800</td>\n",
       "      <td>332</td>\n",
       "      <td>4043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>3332.1800</td>\n",
       "      <td>333</td>\n",
       "      <td>4045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-16</th>\n",
       "      <td>3336.3600</td>\n",
       "      <td>327</td>\n",
       "      <td>4048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-19</th>\n",
       "      <td>3312.6700</td>\n",
       "      <td>333</td>\n",
       "      <td>4050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-20</th>\n",
       "      <td>3328.1000</td>\n",
       "      <td>328</td>\n",
       "      <td>4051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-21</th>\n",
       "      <td>3325.0200</td>\n",
       "      <td>332</td>\n",
       "      <td>4053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22</th>\n",
       "      <td>3312.5000</td>\n",
       "      <td>336</td>\n",
       "      <td>4055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-23</th>\n",
       "      <td>3278.0000</td>\n",
       "      <td>344</td>\n",
       "      <td>4056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>3251.1200</td>\n",
       "      <td>345</td>\n",
       "      <td>4059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27</th>\n",
       "      <td>3254.3200</td>\n",
       "      <td>354</td>\n",
       "      <td>4060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-28</th>\n",
       "      <td>3269.2400</td>\n",
       "      <td>352</td>\n",
       "      <td>4061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29</th>\n",
       "      <td>3272.7300</td>\n",
       "      <td>361</td>\n",
       "      <td>4065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30</th>\n",
       "      <td>3224.5300</td>\n",
       "      <td>389</td>\n",
       "      <td>4067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>3225.1200</td>\n",
       "      <td>391</td>\n",
       "      <td>4068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>3271.0700</td>\n",
       "      <td>372</td>\n",
       "      <td>4068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>3277.4400</td>\n",
       "      <td>374</td>\n",
       "      <td>4068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>3320.1300</td>\n",
       "      <td>362</td>\n",
       "      <td>4071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>3312.1600</td>\n",
       "      <td>364</td>\n",
       "      <td>4072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>3373.7300</td>\n",
       "      <td>355</td>\n",
       "      <td>4073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-10</th>\n",
       "      <td>3360.1500</td>\n",
       "      <td>355</td>\n",
       "      <td>4073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-11</th>\n",
       "      <td>3342.2000</td>\n",
       "      <td>356</td>\n",
       "      <td>4075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-12</th>\n",
       "      <td>3338.6800</td>\n",
       "      <td>353</td>\n",
       "      <td>4077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-13</th>\n",
       "      <td>3310.1000</td>\n",
       "      <td>357</td>\n",
       "      <td>4077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.XSHG  pbcount  total\n",
       "2008-07-15    2779.4500       63   1587\n",
       "2008-07-16    2705.8700       65   1589\n",
       "2008-07-17    2684.7800       65   1589\n",
       "2008-07-18    2778.3700       63   1589\n",
       "2008-07-21    2861.4200       62   1589\n",
       "2008-07-22    2846.1200       62   1589\n",
       "2008-07-23    2837.8500       62   1591\n",
       "2008-07-24    2910.2900       61   1591\n",
       "2008-07-25    2865.1000       62   1591\n",
       "2008-07-28    2903.0100       61   1592\n",
       "2008-07-29    2850.3100       62   1592\n",
       "2008-07-30    2836.6700       62   1592\n",
       "2008-07-31    2775.7200       62   1593\n",
       "2008-08-01    2801.8200       62   1593\n",
       "2008-08-04    2741.7400       62   1593\n",
       "2008-08-05    2690.7500       65   1593\n",
       "2008-08-06    2719.3700       62   1595\n",
       "2008-08-07    2727.5800       63   1595\n",
       "2008-08-08    2605.7200       72   1595\n",
       "2008-08-11    2470.0700       80   1596\n",
       "2008-08-12    2457.2000       82   1596\n",
       "2008-08-13    2446.3000       83   1597\n",
       "2008-08-14    2437.0800       81   1597\n",
       "2008-08-15    2450.6100       81   1597\n",
       "2008-08-18    2319.8700       97   1598\n",
       "2008-08-19    2344.4700       92   1598\n",
       "2008-08-20    2523.2800       77   1598\n",
       "2008-08-21    2431.7200       83   1598\n",
       "2008-08-22    2405.2300       87   1598\n",
       "2008-08-25    2413.3700       86   1598\n",
       "...                 ...      ...    ...\n",
       "2020-09-25    3219.4179      347   4029\n",
       "2020-09-28    3217.5346      353   4036\n",
       "2020-09-29    3224.3600      355   4038\n",
       "2020-09-30    3218.0500      360   4041\n",
       "2020-10-09    3272.0800      339   4041\n",
       "2020-10-12    3358.4700      319   4042\n",
       "2020-10-13    3359.7500      320   4043\n",
       "2020-10-14    3340.7800      332   4043\n",
       "2020-10-15    3332.1800      333   4045\n",
       "2020-10-16    3336.3600      327   4048\n",
       "2020-10-19    3312.6700      333   4050\n",
       "2020-10-20    3328.1000      328   4051\n",
       "2020-10-21    3325.0200      332   4053\n",
       "2020-10-22    3312.5000      336   4055\n",
       "2020-10-23    3278.0000      344   4056\n",
       "2020-10-26    3251.1200      345   4059\n",
       "2020-10-27    3254.3200      354   4060\n",
       "2020-10-28    3269.2400      352   4061\n",
       "2020-10-29    3272.7300      361   4065\n",
       "2020-10-30    3224.5300      389   4067\n",
       "2020-11-02    3225.1200      391   4068\n",
       "2020-11-03    3271.0700      372   4068\n",
       "2020-11-04    3277.4400      374   4068\n",
       "2020-11-05    3320.1300      362   4071\n",
       "2020-11-06    3312.1600      364   4072\n",
       "2020-11-09    3373.7300      355   4073\n",
       "2020-11-10    3360.1500      355   4073\n",
       "2020-11-11    3342.2000      356   4075\n",
       "2020-11-12    3338.6800      353   4077\n",
       "2020-11-13    3310.1000      357   4077\n",
       "\n",
       "[3000 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取每个交易日的上市公司数量\n",
    "df['total'] = [len(get_all_securities(types='stock',date=i)) for i in df.index]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.XSHG</th>\n",
       "      <th>pbcount</th>\n",
       "      <th>total</th>\n",
       "      <th>lower_pb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-07-15</th>\n",
       "      <td>2779.4500</td>\n",
       "      <td>63</td>\n",
       "      <td>1587</td>\n",
       "      <td>0.039698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-16</th>\n",
       "      <td>2705.8700</td>\n",
       "      <td>65</td>\n",
       "      <td>1589</td>\n",
       "      <td>0.040906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-17</th>\n",
       "      <td>2684.7800</td>\n",
       "      <td>65</td>\n",
       "      <td>1589</td>\n",
       "      <td>0.040906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-18</th>\n",
       "      <td>2778.3700</td>\n",
       "      <td>63</td>\n",
       "      <td>1589</td>\n",
       "      <td>0.039648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-21</th>\n",
       "      <td>2861.4200</td>\n",
       "      <td>62</td>\n",
       "      <td>1589</td>\n",
       "      <td>0.039018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-22</th>\n",
       "      <td>2846.1200</td>\n",
       "      <td>62</td>\n",
       "      <td>1589</td>\n",
       "      <td>0.039018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-23</th>\n",
       "      <td>2837.8500</td>\n",
       "      <td>62</td>\n",
       "      <td>1591</td>\n",
       "      <td>0.038969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-24</th>\n",
       "      <td>2910.2900</td>\n",
       "      <td>61</td>\n",
       "      <td>1591</td>\n",
       "      <td>0.038341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-25</th>\n",
       "      <td>2865.1000</td>\n",
       "      <td>62</td>\n",
       "      <td>1591</td>\n",
       "      <td>0.038969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-28</th>\n",
       "      <td>2903.0100</td>\n",
       "      <td>61</td>\n",
       "      <td>1592</td>\n",
       "      <td>0.038317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-29</th>\n",
       "      <td>2850.3100</td>\n",
       "      <td>62</td>\n",
       "      <td>1592</td>\n",
       "      <td>0.038945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-30</th>\n",
       "      <td>2836.6700</td>\n",
       "      <td>62</td>\n",
       "      <td>1592</td>\n",
       "      <td>0.038945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-31</th>\n",
       "      <td>2775.7200</td>\n",
       "      <td>62</td>\n",
       "      <td>1593</td>\n",
       "      <td>0.038920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>2801.8200</td>\n",
       "      <td>62</td>\n",
       "      <td>1593</td>\n",
       "      <td>0.038920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-04</th>\n",
       "      <td>2741.7400</td>\n",
       "      <td>62</td>\n",
       "      <td>1593</td>\n",
       "      <td>0.038920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-05</th>\n",
       "      <td>2690.7500</td>\n",
       "      <td>65</td>\n",
       "      <td>1593</td>\n",
       "      <td>0.040804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-06</th>\n",
       "      <td>2719.3700</td>\n",
       "      <td>62</td>\n",
       "      <td>1595</td>\n",
       "      <td>0.038871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-07</th>\n",
       "      <td>2727.5800</td>\n",
       "      <td>63</td>\n",
       "      <td>1595</td>\n",
       "      <td>0.039498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-08</th>\n",
       "      <td>2605.7200</td>\n",
       "      <td>72</td>\n",
       "      <td>1595</td>\n",
       "      <td>0.045141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-11</th>\n",
       "      <td>2470.0700</td>\n",
       "      <td>80</td>\n",
       "      <td>1596</td>\n",
       "      <td>0.050125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-12</th>\n",
       "      <td>2457.2000</td>\n",
       "      <td>82</td>\n",
       "      <td>1596</td>\n",
       "      <td>0.051378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-13</th>\n",
       "      <td>2446.3000</td>\n",
       "      <td>83</td>\n",
       "      <td>1597</td>\n",
       "      <td>0.051972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-14</th>\n",
       "      <td>2437.0800</td>\n",
       "      <td>81</td>\n",
       "      <td>1597</td>\n",
       "      <td>0.050720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-15</th>\n",
       "      <td>2450.6100</td>\n",
       "      <td>81</td>\n",
       "      <td>1597</td>\n",
       "      <td>0.050720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-18</th>\n",
       "      <td>2319.8700</td>\n",
       "      <td>97</td>\n",
       "      <td>1598</td>\n",
       "      <td>0.060701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-19</th>\n",
       "      <td>2344.4700</td>\n",
       "      <td>92</td>\n",
       "      <td>1598</td>\n",
       "      <td>0.057572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-20</th>\n",
       "      <td>2523.2800</td>\n",
       "      <td>77</td>\n",
       "      <td>1598</td>\n",
       "      <td>0.048185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-21</th>\n",
       "      <td>2431.7200</td>\n",
       "      <td>83</td>\n",
       "      <td>1598</td>\n",
       "      <td>0.051940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-22</th>\n",
       "      <td>2405.2300</td>\n",
       "      <td>87</td>\n",
       "      <td>1598</td>\n",
       "      <td>0.054443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-25</th>\n",
       "      <td>2413.3700</td>\n",
       "      <td>86</td>\n",
       "      <td>1598</td>\n",
       "      <td>0.053817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-25</th>\n",
       "      <td>3219.4179</td>\n",
       "      <td>347</td>\n",
       "      <td>4029</td>\n",
       "      <td>0.086126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-28</th>\n",
       "      <td>3217.5346</td>\n",
       "      <td>353</td>\n",
       "      <td>4036</td>\n",
       "      <td>0.087463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-29</th>\n",
       "      <td>3224.3600</td>\n",
       "      <td>355</td>\n",
       "      <td>4038</td>\n",
       "      <td>0.087915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>3218.0500</td>\n",
       "      <td>360</td>\n",
       "      <td>4041</td>\n",
       "      <td>0.089087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>3272.0800</td>\n",
       "      <td>339</td>\n",
       "      <td>4041</td>\n",
       "      <td>0.083890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-12</th>\n",
       "      <td>3358.4700</td>\n",
       "      <td>319</td>\n",
       "      <td>4042</td>\n",
       "      <td>0.078921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>3359.7500</td>\n",
       "      <td>320</td>\n",
       "      <td>4043</td>\n",
       "      <td>0.079149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-14</th>\n",
       "      <td>3340.7800</td>\n",
       "      <td>332</td>\n",
       "      <td>4043</td>\n",
       "      <td>0.082117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>3332.1800</td>\n",
       "      <td>333</td>\n",
       "      <td>4045</td>\n",
       "      <td>0.082324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-16</th>\n",
       "      <td>3336.3600</td>\n",
       "      <td>327</td>\n",
       "      <td>4048</td>\n",
       "      <td>0.080781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-19</th>\n",
       "      <td>3312.6700</td>\n",
       "      <td>333</td>\n",
       "      <td>4050</td>\n",
       "      <td>0.082222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-20</th>\n",
       "      <td>3328.1000</td>\n",
       "      <td>328</td>\n",
       "      <td>4051</td>\n",
       "      <td>0.080968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-21</th>\n",
       "      <td>3325.0200</td>\n",
       "      <td>332</td>\n",
       "      <td>4053</td>\n",
       "      <td>0.081915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22</th>\n",
       "      <td>3312.5000</td>\n",
       "      <td>336</td>\n",
       "      <td>4055</td>\n",
       "      <td>0.082861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-23</th>\n",
       "      <td>3278.0000</td>\n",
       "      <td>344</td>\n",
       "      <td>4056</td>\n",
       "      <td>0.084813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>3251.1200</td>\n",
       "      <td>345</td>\n",
       "      <td>4059</td>\n",
       "      <td>0.084996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27</th>\n",
       "      <td>3254.3200</td>\n",
       "      <td>354</td>\n",
       "      <td>4060</td>\n",
       "      <td>0.087192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-28</th>\n",
       "      <td>3269.2400</td>\n",
       "      <td>352</td>\n",
       "      <td>4061</td>\n",
       "      <td>0.086678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29</th>\n",
       "      <td>3272.7300</td>\n",
       "      <td>361</td>\n",
       "      <td>4065</td>\n",
       "      <td>0.088807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30</th>\n",
       "      <td>3224.5300</td>\n",
       "      <td>389</td>\n",
       "      <td>4067</td>\n",
       "      <td>0.095648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>3225.1200</td>\n",
       "      <td>391</td>\n",
       "      <td>4068</td>\n",
       "      <td>0.096116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>3271.0700</td>\n",
       "      <td>372</td>\n",
       "      <td>4068</td>\n",
       "      <td>0.091445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>3277.4400</td>\n",
       "      <td>374</td>\n",
       "      <td>4068</td>\n",
       "      <td>0.091937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>3320.1300</td>\n",
       "      <td>362</td>\n",
       "      <td>4071</td>\n",
       "      <td>0.088922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>3312.1600</td>\n",
       "      <td>364</td>\n",
       "      <td>4072</td>\n",
       "      <td>0.089391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>3373.7300</td>\n",
       "      <td>355</td>\n",
       "      <td>4073</td>\n",
       "      <td>0.087159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-10</th>\n",
       "      <td>3360.1500</td>\n",
       "      <td>355</td>\n",
       "      <td>4073</td>\n",
       "      <td>0.087159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-11</th>\n",
       "      <td>3342.2000</td>\n",
       "      <td>356</td>\n",
       "      <td>4075</td>\n",
       "      <td>0.087362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-12</th>\n",
       "      <td>3338.6800</td>\n",
       "      <td>353</td>\n",
       "      <td>4077</td>\n",
       "      <td>0.086583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-13</th>\n",
       "      <td>3310.1000</td>\n",
       "      <td>357</td>\n",
       "      <td>4077</td>\n",
       "      <td>0.087564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.XSHG  pbcount  total  lower_pb\n",
       "2008-07-15    2779.4500       63   1587  0.039698\n",
       "2008-07-16    2705.8700       65   1589  0.040906\n",
       "2008-07-17    2684.7800       65   1589  0.040906\n",
       "2008-07-18    2778.3700       63   1589  0.039648\n",
       "2008-07-21    2861.4200       62   1589  0.039018\n",
       "2008-07-22    2846.1200       62   1589  0.039018\n",
       "2008-07-23    2837.8500       62   1591  0.038969\n",
       "2008-07-24    2910.2900       61   1591  0.038341\n",
       "2008-07-25    2865.1000       62   1591  0.038969\n",
       "2008-07-28    2903.0100       61   1592  0.038317\n",
       "2008-07-29    2850.3100       62   1592  0.038945\n",
       "2008-07-30    2836.6700       62   1592  0.038945\n",
       "2008-07-31    2775.7200       62   1593  0.038920\n",
       "2008-08-01    2801.8200       62   1593  0.038920\n",
       "2008-08-04    2741.7400       62   1593  0.038920\n",
       "2008-08-05    2690.7500       65   1593  0.040804\n",
       "2008-08-06    2719.3700       62   1595  0.038871\n",
       "2008-08-07    2727.5800       63   1595  0.039498\n",
       "2008-08-08    2605.7200       72   1595  0.045141\n",
       "2008-08-11    2470.0700       80   1596  0.050125\n",
       "2008-08-12    2457.2000       82   1596  0.051378\n",
       "2008-08-13    2446.3000       83   1597  0.051972\n",
       "2008-08-14    2437.0800       81   1597  0.050720\n",
       "2008-08-15    2450.6100       81   1597  0.050720\n",
       "2008-08-18    2319.8700       97   1598  0.060701\n",
       "2008-08-19    2344.4700       92   1598  0.057572\n",
       "2008-08-20    2523.2800       77   1598  0.048185\n",
       "2008-08-21    2431.7200       83   1598  0.051940\n",
       "2008-08-22    2405.2300       87   1598  0.054443\n",
       "2008-08-25    2413.3700       86   1598  0.053817\n",
       "...                 ...      ...    ...       ...\n",
       "2020-09-25    3219.4179      347   4029  0.086126\n",
       "2020-09-28    3217.5346      353   4036  0.087463\n",
       "2020-09-29    3224.3600      355   4038  0.087915\n",
       "2020-09-30    3218.0500      360   4041  0.089087\n",
       "2020-10-09    3272.0800      339   4041  0.083890\n",
       "2020-10-12    3358.4700      319   4042  0.078921\n",
       "2020-10-13    3359.7500      320   4043  0.079149\n",
       "2020-10-14    3340.7800      332   4043  0.082117\n",
       "2020-10-15    3332.1800      333   4045  0.082324\n",
       "2020-10-16    3336.3600      327   4048  0.080781\n",
       "2020-10-19    3312.6700      333   4050  0.082222\n",
       "2020-10-20    3328.1000      328   4051  0.080968\n",
       "2020-10-21    3325.0200      332   4053  0.081915\n",
       "2020-10-22    3312.5000      336   4055  0.082861\n",
       "2020-10-23    3278.0000      344   4056  0.084813\n",
       "2020-10-26    3251.1200      345   4059  0.084996\n",
       "2020-10-27    3254.3200      354   4060  0.087192\n",
       "2020-10-28    3269.2400      352   4061  0.086678\n",
       "2020-10-29    3272.7300      361   4065  0.088807\n",
       "2020-10-30    3224.5300      389   4067  0.095648\n",
       "2020-11-02    3225.1200      391   4068  0.096116\n",
       "2020-11-03    3271.0700      372   4068  0.091445\n",
       "2020-11-04    3277.4400      374   4068  0.091937\n",
       "2020-11-05    3320.1300      362   4071  0.088922\n",
       "2020-11-06    3312.1600      364   4072  0.089391\n",
       "2020-11-09    3373.7300      355   4073  0.087159\n",
       "2020-11-10    3360.1500      355   4073  0.087159\n",
       "2020-11-11    3342.2000      356   4075  0.087362\n",
       "2020-11-12    3338.6800      353   4077  0.086583\n",
       "2020-11-13    3310.1000      357   4077  0.087564\n",
       "\n",
       "[3000 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 破净率 = 破净上市公司数量 / 总的上市公司数量\n",
    "df['lower_pb'] = df['pbcount']/df['total']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['lower_pb']\n",
    "x = df.index\n",
    "\n",
    "y2 = df['000001.XSHG']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 图层一 ： 破净股比例\n",
    "fig,ax = plt.subplots(1,1,figsize =(10,8))\n",
    "ax.plot(x,y,color='b',label = 'lower_pb')\n",
    "plt.legend(loc=1)\n",
    "plt.title('破净股比例与上证指数对比图')\n",
    "\n",
    "# 图层二： 上证指数\n",
    "ax1 = ax.twinx()\n",
    "ax1.plot(x,y2,color = 'r', label = '上证指数')\n",
    "plt.legend(loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 各行业市场估值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 要查询的各板块股票\n",
    "index_list = ['000001.XSHG','000002.XSHG','000003.XSHG','000905.XSHG','399997.XSHE','000808.XSHG','399006.XSHE','399678.XSHE','399005.XSHE']\n",
    "index_name= ['上证指数','A股指数','B股指数','中证500','中证白酒指数','医药生物','创业板','次新股','中小板']\n",
    "\n",
    "# 截取部分数据\n",
    "date_list = [ i for i in df.index[::-30]]\n",
    "\n",
    "pe_fin = []\n",
    "for i in date_list:\n",
    "    pe_index = []\n",
    "    for j in index_list:\n",
    "        stock_list = get_index_stocks(j,i)\n",
    "        # 获取各版块股票信息\n",
    "        df_temp = get_fundamentals(query(valuation.code,valuation.pe_ratio).filter(valuation.code.in_(stock_list)),date=i)\n",
    "        # 取二等分位值，语法：quantile(分位值小数)\n",
    "        pe = df_temp['pe_ratio'].quantile(0.5)\n",
    "        pe_index.append(pe)\n",
    "    pe_fin.append(pe_index)\n",
    "pe_df = pd.DataFrame(pe_fin,index=date_list,columns=index_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f426c98f860>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 按照日期进行升序排列\n",
    "data = pe_df.sort_index(ascending = 1)\n",
    "# 绘制图案\n",
    "data.plot(figsize= (10,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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